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Regression Introduction • We will introduce multiple regression, in particular we will: • Learn when we can use multiple regression • Learn how multiple regression extends simple regression • Learn how to use multiple regression in real applications • This presentation is intended for students in initial stages of Statistics. No previous knowledge is required. It is advised to first read the presentation on simple linear regression. 2 Regression • Regression is used to study the relationship between one dependent variable and two or more independent variables. • Just as in single regression, we need the dependent variable to be numerical. The independent variables can be numerical or categorical. •However, if all the independent variables are categorical, it is best to use ANOVA. 3 Motivation • Single regression allows us to study the relationship between two variables only. • However, in reality, we do not believe that only a single variable explains all the variation of the dependent variable. • For example, in the scenario of IQ and income, we do not expect IQ only to explain income, but we expect that there are also other variables, such as level of education, to explain income. • Hence, to make the model more realistic, it makes sense to include multiple independent variables in the regression. 4 Examples The following are situations where we can use multiple regression: • Testing if IQ and level of education affect income (IQ and level of education are the IV and income is the DV). • Testing if hours of work and level of stress affect hours of sleep (DV is hours of sleep, and the hours of work and level of stress are the IV). •Testing if the number of cigarettes smoked and amount of salt in the diet affect blood pressure (number of cigarettes smoked and salt are the IV and blood pressure is the DV). 5 Displaying the data As opposed to the simple linear regression case, we do not have a way to plot all the variables at the same time. Hence, the scatterplot can be performed only for each continuous independent variable independently. 6
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